NMR chemical shifts provide important local structural information for proteins. Consistent structure generation from NMR chemical shift data has become feasible for proteins with sizes of up to about 100-130 residues, and such structures are of a quality comparable to those obtained with the standard NMR protocol. In collaboration with Dr. David Baker and his group, we developed a chemical-shift-guided approach to successfully and accurately determine structures on the basis of chemical shifts, but in practice the approach was limited to relatively small proteins. Recent work has focused on extending this approach to allow incorporation of easily accessible experimental information and to more extensively exploit the available database of previously solved structures. This work has culminated in the program POMONA, which is made available to the community world-wide as a webserver. Considerable effort has been spent on further optimizing this service and trouble shooting the problems the user community encounters when applying this method to non-standard system. In a separate effort, we have developed a new and highly robust algorithm for Sparse Multidimensional Iterative Lineshape-Enhanced (SMILE) reconstruction of both non-uniformly sampled and conventional NMR data. Although the concept of only sampling a small subset of the time domain NMR data, followed by non-linear processing, was introduced three decades ago, none of the reconstruction algorithms were sufficiently robust for challenging applications. The new program developed represents a large step forward and has been seamlessly integrated in the NIDDK-developed processing package, NMRPipe, which is used world-wide for the majority of advanced multi-dimensional (3D and 4D) processing schemes. For large data sets, the method is robust and demonstrated for sparsities down to ca 1%, and final all-real spectral sizes as large as 300 Gb. Comparison between fully sampled, conventionally processed spectra and randomly selected NUS subsets of this data shows that the reconstruction quality approaches the theoretical limit in terms of peak position fidelity and intensity. SMILE essentially removes the noise-like appearance associated with the point-spread function of signals that are a default of five-fold above the noise level, but impacts the actual thermal noise in the NMR spectra only minimally. Therefore, the appearance and interpretation of SMILE-reconstructed spectra is very similar to that of fully sampled spectra generated by Fourier transformation.